CN102969720B

CN102969720B - A kind of load Dynamic controlling that can apply in intelligent grid and analytical method

Info

Publication number: CN102969720B
Application number: CN201210431915.9A
Authority: CN
Inventors: 刘云; 刘晨旭; 曾庆安; 张振江; 程紫尧; 邓磊; 马腾
Original assignee: Beijing Jiaotong University
Current assignee: Beijing Jiaotong University
Priority date: 2012-11-01
Filing date: 2012-11-01
Publication date: 2016-01-20
Anticipated expiration: 2032-11-01
Also published as: CN102969720A

Abstract

A method for dynamic control and analysis of smart grid loads based on wireless sensor networks, comprising the following steps: Communication network analysis: analyzing the performance of the communication network of the smart grid according to the data periodically transmitted by the load combined with the data collected in real time, and obtaining the current The performance of the elements that affect the performance of the smart grid; dynamic load analysis and control model establishment: according to the information obtained from the load and the performance of the communication network, the corresponding dynamic load analysis and control model is established, and the current data of the load and the stored historical data are added. Analyze and predict the future power consumption status of the load; load processing: based on the results of the dynamic load analysis and control model prediction, optimally control the power consumption allocation of the load.

Description

A load dynamic control and analysis method that can be applied in smart grid

技术领域 technical field

本发明涉及一种能够在智能电网中应用的负载动态控制和分析方法，属于通信和信息技术领域。The invention relates to a load dynamic control and analysis method that can be applied in a smart grid, and belongs to the field of communication and information technology.

背景技术 Background technique

能量问题已经成为当今世界的重要话题之一。如何更加高效地利用能量，如何更加有效的节约能量，已经成为了世界众多企业和科研组织研究的最热点科目。近期，许多国家都提出了一揽子计划，鼓励企业和科研组织将更大的精力投入智能电网领域的研究和应用。目前来说，电网已成为工业化、信息化社会发展的基础和重要组成部分。但在当今经济的发展趋势下，传统电网已经不能够满足电力行业的需求，无法实现高效的利用能量，智能电网成为了未来世界电网发展的必然方向。简单地说，智能电网结合了传统电网、信息和通信网络，三者一体，互相配合，互相促进，实现了电网服务的高效性、可靠性、安全性，减少了运营和管理的成本，达到了节能的目的。智能电网是将先进的传感测量技术、信息通信技术、分析决策技术、自动控制技术和能源电力技术相结合，实现了与电网基础设施高度集成的新型现代化电网。Energy issues have become one of the important topics in today's world. How to use energy more efficiently and how to save energy more effectively has become the hottest subject of research by many companies and scientific research organizations around the world. Recently, many countries have proposed a package plan to encourage enterprises and scientific research organizations to devote more energy to the research and application of smart grid. At present, the power grid has become the foundation and an important part of the development of industrialization and information society. However, under the development trend of today's economy, the traditional power grid can no longer meet the needs of the power industry and cannot achieve efficient energy use. Smart grids have become an inevitable direction for the development of the world's power grids in the future. Simply put, the smart grid combines the traditional power grid, information and communication network, the three are integrated, cooperate with each other, and promote each other, realize the efficiency, reliability, and safety of grid services, reduce the cost of operation and management, and achieve The purpose of energy saving. Smart grid is a new type of modern grid that is highly integrated with grid infrastructure by combining advanced sensing and measurement technology, information and communication technology, analysis and decision-making technology, automatic control technology and energy and power technology.

作为世界电网进行发展的必然趋势和新趋势，不管国内还是国外，都对智能电网给予高度的重视和极大的关注。在我国，智能电网的发展前景一片大好。我国国家电网公司已经提出了将特高压电网作为一种骨干网架，我国各级电网之间协调发展，以这一坚实的电网作为基础，以信息通信平台为支撑，以智能控制为手段，对各个环节加以控制和优化，其中包括电力系统的发电、输电、变电、配电、用电和调度等，充分地利用先进的控制技术、信息技术以及通信技术，构建以互动化、数字化、自动化以及信息化为主要特征的具有中用特色的、自主创新、国际领先智能化的电网，实现“电力流、信息流、业务流”的高度一体化融合，实现供能的精确化、对应化、互助化和互补化，保证现代化电网的服务达到坚强可靠、经济高效、清洁环保、透明开放、友好互动的目标。随着市场化改革的不断推进，智能电网已成为现代电网技术发展的必由之路。As the inevitable trend and new trend of the development of the world power grid, no matter at home or abroad, the smart grid is highly valued and paid great attention to. In my country, the development prospect of smart grid is bright. The State Grid Corporation of my country has proposed to use the UHV power grid as a backbone grid, and coordinate the development of power grids at all levels in my country. Based on this solid power grid, supported by information and communication platforms, and intelligently controlled by means of Control and optimize each link, including power generation, transmission, transformation, distribution, consumption, and dispatching of the power system, making full use of advanced control technology, information technology, and communication technology to build an interactive, digital, and automated system. As well as the information-based power grid with Chinese characteristics, independent innovation, and international leading intelligence, it will realize the highly integrated integration of "power flow, information flow, and business flow", and realize the precise, corresponding, and efficient energy supply. Mutual aid and complementarity ensure that the services of the modern power grid achieve the goal of being strong and reliable, cost-effective, clean and environmentally friendly, transparent and open, and friendly and interactive. With the continuous advancement of market-oriented reforms, smart grid has become the only way for the development of modern grid technology.

众所周知，智能电网的发展和应用涉及到了很多领域的相关技术，其中，最为核心的技术之一就是信息及通信技术。2009年，由IEEE发起的，并提出了Project2030计划，制定了一系列关于智能电网的国际标准。在Project2030计划中，明确指出智能电网包括三个部分：能量技术、通信技术和信息技术。因此，通信和信息技术作为智能电网极其重要的核心技术，同时也是智能电网得以实现的基础，如何应用信息和通信技术，实现现代电网的智能化，利用电网的实时信息，智能的调整和平衡能量供应，已经成为了信息和通信技术当前在智能电网领域的研究热点。As we all know, the development and application of smart grid involves related technologies in many fields, among which one of the core technologies is information and communication technology. In 2009, it was initiated by IEEE, and the Project2030 plan was put forward to formulate a series of international standards on smart grid. In the Project2030 plan, it is clearly pointed out that the smart grid includes three parts: energy technology, communication technology and information technology. Therefore, communication and information technology, as an extremely important core technology of smart grid, is also the basis for the realization of smart grid. How to apply information and communication technology to realize the intelligence of modern power grid, and use real-time information of power grid to intelligently adjust and balance energy Supply has become a research hotspot of information and communication technology in the field of smart grid.

当前智能电网领域的通信和信息技术研究多集中在通信和信息系统的框架上，并没有实际的智能电网实时控制和分析负载的模型和方法，因此存在这样一种技术需求，即，需要一种分析和控制方法，能够通过通信网，实时的监控当前电网中的负载信息，智能地通过历史数据和当前数据对负载的用电情况进行分析，并通过通信网来控制负载的用电情况。The current communication and information technology research in the field of smart grid is mostly focused on the framework of communication and information systems, and there is no actual smart grid real-time control and load analysis model and method, so there is such a technical demand, that is, a The analysis and control method can monitor the load information in the current power grid in real time through the communication network, intelligently analyze the power consumption of the load through historical data and current data, and control the power consumption of the load through the communication network.

发明内容 Contents of the invention

鉴于以上所述的现有技术的局限性，本发明公开了一种能够在智能电网中应用的负载动态控制和分析的方法，对智能电网中的负载进行实时的控制和分析，从而达到节能的目的。In view of the limitations of the prior art described above, the present invention discloses a load dynamic control and analysis method that can be applied in the smart grid, and performs real-time control and analysis on the load in the smart grid, so as to achieve energy saving Purpose.

一种基于无线传感器网络的智能电网负载动态控制和分析方法，包括以下步骤：A method for dynamic control and analysis of smart grid loads based on wireless sensor networks, comprising the following steps:

1)通信网络分析：根据负载的周期性传输的数据结合实时采集的数据，对智能电网的通信网络性能进行分析，获取当前影响智能电网性能的要素的性能；1) Communication network analysis: analyze the performance of the communication network of the smart grid based on the periodically transmitted data of the load combined with the data collected in real time, and obtain the performance of the elements that currently affect the performance of the smart grid;

2)动态负载分析和控制模型建立：根据获取负载的信息和通信网络的性能，建立相应的负载动态控制和分析模型，对负载的当前数据和所存储的历史数据加以分析，预测负载未来的用电状况；2) Establishment of dynamic load analysis and control model: according to the obtained load information and the performance of the communication network, establish a corresponding load dynamic control and analysis model, analyze the current data of the load and the stored historical data, and predict the future usage of the load. electrical status;

3)负载处理：基于所述动态负载分析和控制模型预测的结果，对负载的用电调配进行优化控制。3) Load processing: based on the results of dynamic load analysis and control model prediction, optimal control is performed on the allocation of load power consumption.

所述的通信网络分析步骤包括：Described communication network analysis step comprises:

a)数据采集及传输步骤，实时采集智能电网负载的数据信息并将所采集到的信息传送到智能控制分析中心；a) Data collection and transmission steps, collecting data information of smart grid loads in real time and transmitting the collected information to the intelligent control analysis center;

b)通信网络性能分析步骤，依据电网数据信息、数据发送及所需速率导致的通信网络的时延、阻塞来分析数据采集速率对整个通信网络性能的影响，进而分析当前通信网络的性能。b) The communication network performance analysis step is to analyze the impact of the data collection rate on the performance of the entire communication network according to the delay and congestion of the communication network caused by the grid data information, data transmission and required rate, and then analyze the performance of the current communication network.

所述负载动态控制和分析模型建立步骤中，依据所获取的数据信息和影响智能电网效率的要素，建立对智能电网中用户负载的动态控制和分析模型。In the step of establishing the load dynamic control and analysis model, a dynamic control and analysis model for user loads in the smart grid is established based on the acquired data information and factors affecting the efficiency of the smart grid.

所述的负载处理步骤中，信息中心根据所构建的动态控制模型结果结合负载的实时数据信息对负载未来的用电信息进行一定的预测，进而对智能电网中的用户用电情况进行分析进而对电网输送情况进行重新调配，这样既可以保障当前用户的用电，又能使剩余的电量能够提供给其他更需要用电的用户，从而能够很好地控制负载的用电状况，提高负载用电的效率。In the load processing step, the information center predicts the future power consumption information of the load according to the result of the dynamic control model constructed and the real-time data information of the load, and then analyzes the power consumption situation of the users in the smart grid and further analyzes According to the grid transmission situation, it can not only guarantee the power consumption of current users, but also make the remaining power available to other users who need more power, so as to control the power consumption of loads well and improve the power consumption of loads. s efficiency.

所述的负载处理步骤中，分析影响电能节约率的要素包括数据的采集频率、数据的发送速率、区域内节点的数量多少（即数据中心的覆盖范围）、节点状态的改变频率（即用户状态的稳定状况）和网络时延和阻塞。In the load processing step, the analysis of factors affecting the power saving rate includes the frequency of data collection, the rate of data transmission, the number of nodes in the area (that is, the coverage of the data center), and the frequency of changes in node status (that is, the user status stability) and network delay and congestion.

所述的动态负载分析和控制模型如下：The dynamic load analysis and control model is as follows:

$C C = = {Σ Σ}_{i i = = 11}^{n no} {W W}_{i i} {S S}_{i i} - - - - - - ((11))$

0≤W_i≤1,0≤S_i≤1(2)0≤W _i ≤1,0≤S _i ≤1(2)

其中，C表示该用户的对于电能的消费指数,S_i是影响智能电网用电效率的因素，W_i是对应影响因素的权重，Among them, C represents the user's consumption index of electric energy, S _i is the factor that affects the power consumption efficiency of the smart grid, and W _i is the weight of the corresponding influencing factors,

具体模型如式（15）、（16）、（17）所示：The specific model is shown in formulas (15), (16) and (17):

C=W_RS_R+W_VS_V+W_LS_L+W_PS_P(3)C=W _R S _R +W _V S _V +W _L S _L +W _P S _P (3)

0≤S_R,S_V,S_L,S_P≤1(4)0≤S _R ,S _V ,S _L ,S _P ≤1(4)

0≤W_R,W_V,W_L,W_P≤1(5)0≤W _R ,W _V ,W _L ,W _P ≤1(5)

W_R+W_V+W_L+W_P=1W _R +W _V +W _L +W _P =1

(6)(6)

其中，in,

W_R,W_V,W_L,W_P表示四种影响智能电网用电效率的各个因素S_R,S_V,S_L,S_P在当前分析和预测评估体系中所占的重要性,所有因素的对应权重W_R,W_V,W_L,W_P之和等于1；W _R , W _V , W _L , W _P represent the importance of the four factors S _R , S _V , S _L , and S _P that affect the power efficiency of the smart grid in the current analysis and prediction evaluation system. All factors The sum of corresponding weights W _R , W _V , W _L , W _P is equal to 1;

S_R是每个用户的负载用电量的过度的比例，表示为W_r是没有使用的电能，W_{max_r}是用户电能富裕的最大值；S _R is the excess proportion of the load power consumption of each user, expressed as W _r is the unused electric energy, and W _{max_r} is the maximum value of the user's electric energy abundance;

S_v是每个用户的负载用电量的波动情况，表示为T_ave是用户用电状况不同状态之间的转变的时间间隔，T_{max_v}表示为用户用电状况不同状态之间的转变的最大时间间隔；S _v is the fluctuation of the load power consumption of each user, expressed as T _ave is the time interval between transitions between different states of the user's power consumption status, and T _{max_v} is expressed as the maximum time interval between transitions between different states of the user's power consumption status;

S_L是每个用户能量传输的损失比例，表示为d是发电厂和能量短缺的用户之间的距离，d_max是最大距离； _SL is the proportion of loss in energy transfer for each user, expressed as d is the distance between the power plant and the energy-deficient user, d _max is the maximum distance;

S_P是用户用电的优先级。S _P is the priority of the user's electricity consumption.

所述的网络时延和阻塞概率的计算步骤为：The calculation steps of the network delay and blocking probability are:

针对每一个节点，平时时延为T_D，代表节点试图发出数据到数据被控制信息中心收到之间的时间，无线传感器网络的服务时间为T_S，网络中数据的等待时间为T_W，三者关系如下：For each node, the usual delay is T _D , which represents the time between when the node tries to send data and the data is received by the control information center. The service time of the wireless sensor network is T _S , and the waiting time of data in the network is T _W . The relationship between the three is as follows:

T_D=T_S+T_W(7)T _D =T _S +T _W (7)

其中，in,

T_S=T_L/T_V+T_C+T_P(8)T _S =T _L /T _V +T _C +T _P (8)

T_L是数据包的长度，T_V是数据的发送速率，T_C是MAC时延，T_P是无线传输时延。其中，MAC时延，即介质访问控制时延，表示数据从得到智能电表允许发送起，到能够发送到信道中的时间，也就是数据获取信道的时间； _TL is the length of the data packet, T _V is the data transmission rate, T _C is the MAC delay, and T _P is the wireless transmission delay. Among them, the MAC delay, that is, the medium access control delay, indicates the time from when the data is allowed to be sent by the smart meter to when it can be sent to the channel, that is, the time for the data to acquire the channel;

T_Ck是数据从节点i到节点j的第k个路径上的MAC时延，T_Wk是数据从节点i到节点j的第k个路径上的服务时间；T _Ck is the MAC delay on the k-th path of data from node i to node j, and T _Wk is the service time on the k-th path of data from node i to node j;

因此，无线传感器网络中的端到端总体时延为Therefore, the overall end-to-end delay in wireless sensor networks is

${T T}_{Delay Delay__overall overall} = = \underset{R R}{Σ Σ} (({Delay Delay}_{R R} \cdot \cdot {γ γ}_{i i,, j j})) = = \underset{R R}{Σ Σ} ((\underset{((ij ij,, k k)) &Element; &Element; R R}{Σ Σ} (({T T}_{Ck C} + + {T T}_{Wk w} + + {T T}_{L L} / / {T T}_{V V})) \cdot \cdot {γ γ}_{ij ij,, k k})) - - - - - - ((99))$

其中，in,

${Delay Delay}_{R R} = = \underset{i i &Element; &Element; R R}{Σ Σ} {D D.}_{i i} - - - - - - ((1010))$

Delay_R代表基于路径R的总平均时延，γ_i，j代表数据从节点i到节点j所有路径的比例集合，γ_ij,k代表数据从节点i到节点j第k个路径的比例，两者的关系如下：Delay _R represents the total average delay based on path R, γ _i,j represents the proportion set of all paths from node i to node j, γ _ij,k represents the proportion of the kth path from node i to node j, two The relationship is as follows:

${γ γ}_{i i,, j j} = = \underset{((ij ij,, k k)) &Element; &Element; R R}{Σ Σ} {γ γ}_{ij ij,, k k} - - - - - - ((1111))$

根据文献(YanYe,CaiHua,SeoSeung-Woo,PerformanceAnalysisiofIEEE802.11Wirelesssensornetworks[C],IEEEInternationalConferenceonCommunications,pp.2547-2551,2008),MAC时延的生成概率母函数如下：According to the literature (YanYe, CaiHua, SeoSeung-Woo, Performance Analysis of IEEE802.11 Wireless sensornetworks [C], IEEE International Conference on Communications, pp.2547-2551, 2008), the generation probability generating function of MAC delay is as follows:

${T T}_{MAC MAC} ((Z Z)) = = ((11 - - p p)) S S ((Z Z)) {Σ Σ}_{i i = = 00}^{L L} {{{[[PI P.I. ((Z Z))]]}^{i i} {Π Π}_{j j = = 00}^{i i} {D D.}_{j j} ((Z Z))}} + + {[[pI pI ((Z Z))]]}^{L L + + 11} {Π Π}_{i i = = 00}^{L L} {D D.}_{i i} ((Z Z)) - - - - - - ((1212))$

p指的是某对节点的通信与冲突域内其他通信发生冲突的概率，具体表示如下：p refers to the probability that the communication of a certain pair of nodes collides with other communication in the conflict domain, which is expressed as follows:

p=1-(1-τ)^n-1(13)p=1-(1-τ) ^n-1 (13)

$S S ((Z Z)) = = {Z Z}^{{T T}_{S S}},, I I ((Z Z)) = = {Z Z}^{{T T}_{I I}} and and {D D.}_{i i} ((Z Z)) = = \begin{matrix} \{\begin{matrix} {Σ Σ}_{i i = = 00}^{{CW CW}_{i i} - - 11} \frac{D D. ((Z Z))}{{CW CW}_{i i}},, 00 \leq \leq i i \leq \leq m m \\ {D D.}_{m m} ((Z Z)),, m m \leq \leq i i \leq \leq L L \end{matrix} - - - - - - ((1414)) \end{matrix}$

得到MAC时延的期望和方差Get the expectation and variance of the MAC delay

(15)(15)

E(T_MAC)=T′_MAC(Z)|_Z=1 E(T _MAC )=T′ _MAC (Z)| _Z=1

Var(T_MAC)=T″_MAC(Z)_Z＝1+T′_MAC(Z)|_Z=1-[T_MAC(Z)|_Z=1]²(16)Var(T _MAC )=T″ _MAC (Z) _Z＝1 +T′ _MAC (Z)| _Z=1 -[T _MAC (Z)| _Z=1 ] ² (16)

其中，in,

L表示最大的检测信道的次数；L represents the maximum number of detection channels;

自变量Z是将队列上的离散问题进行Z变换后，将问题转化为Z域上变成连续的问题以便进行数学解决，变量Z本身没有什么实际意义；The independent variable Z is to transform the discrete problem on the queue into a continuous problem in the Z domain for mathematical solution. The variable Z itself has no practical significance;

等待时间的计算如下：The waiting time is calculated as follows:

首先，定义到达负载的传输密度：First, define the transport density to the load:

$ρ ρ = = λb λb = = \frac{λ λ}{μ μ} - - - - - - ((1717))$

其中，λ是数据到达速率，μ是数据包的服务速率，b是数据包服务时间的期望值；where λ is the data arrival rate, μ is the service rate of the data packet, and b is the expected value of the data packet service time;

设η是数据包丢失的概率，P_k是队列中存在k个数据包的平均概率；E(X)和E(T)是服务队列的数据的平均数量和平均时延；Let n be the probability of data packet loss, and P _k be the average probability of having k data packets in the queue; E(X) and E(T) are the average quantity and the average time delay of the data of the service queue;

以上参数的关系如下：The relationship between the above parameters is as follows:

η＝λ(1-P_k)(18)η=λ(1-P _k )(18)

E(X)=rE(T)(19)E(X)=rE(T)(19)

根据文献（CassandrasChristosG.,LafortuneStephane,“IntroductiontoDiscreteEventSystems”,USA,2009.）中的Pollaczek-Khinchin公式，得出：According to the Pollaczek-Khinchin formula in the literature (CassandrasChristosG., LafortuneStephane, "Introduction to DiscreteEventSystems", USA, 2009.), it is obtained:

$E E. ((X x)) = = \frac{ρ ρ}{11 - - ρ ρ} - - \frac{{ρ ρ}^{22}}{22 ((11 - - ρ ρ))} ((11 - - {μ μ}^{22} {σ σ}^{22})) - - - - - - ((2020))$

其中，σ²是服务时间的方差，X是队列的长度；因此，得到服务时间的期望值E(T_W):where ^σ2 is the variance of the service time and X is the length of the queue; thus, the expected value of the service time E(T _W ):

$E E. (({T T}_{W W})) = = E E. ((T T)) - - b b = = \frac{11}{η η} E E. ((X x)) - - b b - - - - - - ((21 twenty one))$

计算出整个网络中的阻塞概率：Calculate the blocking probability in the entire network:

${P P}_{block block} = = {P P}_{K K} = = 11 - - \frac{11}{{π π}_{00} - - ρ ρ} - - - - - - ((22 twenty two))$

其中，π₀表示数据包进入服务队列时，整个队列的初始状态。Among them, π ₀ represents the initial state of the entire queue when the data packet enters the service queue.

所述方法的流程图如图1所示。The flowchart of the method is shown in FIG. 1 .

本发明的有益效果是，针对现有的智能电网的发展趋势，对智能电网在未来信息通信网络的建设和节能预测、分析模型的创新上提供一种可利用的、高效的技术支持和借鉴，使得智能电网能够更好、更快的发展起来。本发明能够结合信息通信网络的性能和电网负载的历史和当前状况，进行有效的数据传输、电能输送调节，保证信息通信网络能够高效的支持智能电网的运行，保证智能电网中各负载的用电情况得到准确、有针对性的分析和预测，确保负载在未来的一段时间内高效的利用电能，提高能量的利用率。而对于有可能造成大量电能浪费的情况，进行一定程度的预防和调节，从而达到智能电网建设的最终目的——提供坚强可靠、经济高效、清洁环保、透明开放、友好互动的现代化电网服务。The beneficial effect of the present invention is that, aiming at the development trend of the existing smart grid, it provides an available and efficient technical support and reference for the construction of the future information communication network and the innovation of energy-saving prediction and analysis models for the smart grid. Make smart grid can develop better and faster. The present invention can combine the performance of the information communication network and the history and current status of the load of the power grid to perform effective data transmission and power transmission adjustment, ensure that the information communication network can efficiently support the operation of the smart grid, and ensure the power consumption of each load in the smart grid The situation is accurately and targetedly analyzed and predicted to ensure that the load can efficiently use electric energy in the future and improve the utilization rate of energy. For situations that may cause a large amount of waste of electric energy, a certain degree of prevention and adjustment is carried out, so as to achieve the ultimate goal of smart grid construction - to provide modern grid services that are strong, reliable, cost-effective, clean, environmentally friendly, transparent, open, and friendly.

附图说明 Description of drawings

图1为基于无线传感器网络的智能电网负载动态控制和分析方法的流程概述图；Fig. 1 is an overview of the flow of the smart grid load dynamic control and analysis method based on the wireless sensor network;

图2为基于无线传感器网络的智能电网负载动态控制和分析方法的具体流程图；Fig. 2 is the specific flowchart of the load dynamic control and analysis method of the smart grid based on the wireless sensor network;

图3为智能电网的网络拓扑示意图；FIG. 3 is a schematic diagram of a network topology of a smart grid;

图4为随着节点数量的改变，通信网络端到端的时延的性能图；Figure 4 is a performance diagram of the end-to-end delay of the communication network as the number of nodes changes;

图5为随着节点数量的改变，通信网络数据发生阻塞概率的性能图；FIG. 5 is a performance diagram of the blocking probability of communication network data as the number of nodes changes;

图6为随着节点数量的改变，能量利用效率的性能比较图；Figure 6 is a performance comparison diagram of energy utilization efficiency as the number of nodes changes;

图7为随着数据采集时间的改变，能量利用效率的性能比较图；Figure 7 is a performance comparison diagram of energy utilization efficiency as the data collection time changes;

图8为节点数量和数据发送速率的改变对能量的利用效率的影响比较图；Fig. 8 is a comparison diagram of the impact of changes in the number of nodes and the data transmission rate on energy utilization efficiency;

图9为数据采集速率和发送速率的改变对能量的利用效率的影响比较图；Fig. 9 is a comparison diagram of the impact of changes in the data acquisition rate and transmission rate on energy utilization efficiency;

图10为数据采集速率和节点数量的改变对负载能量的需求控制的精准性。Figure 10 shows the accuracy of load energy demand control with changes in the data collection rate and the number of nodes.

具体实施方式 detailed description

基于无线传感器网络的负载动态控制和分析模型的性能主要是通过能量节省的效率来衡量的。能量节省的效率是负载节约的能量与负载不使用模型时使用的能量的比值。若M为不使用该动态控制模型时负载使用的能量，T为使用该动态控制模型时负载使用的能量，则能量节省的效率为PowerSaving=M-TM。The performance of load dynamic control and analysis models based on wireless sensor networks is mainly measured by the efficiency of energy saving. The efficiency of energy saving is the ratio of the energy saved by the load to the energy used when the load is not using the model. If M is the energy used by the load when the dynamic control model is not used, and T is the energy used by the load when the dynamic control model is used, then the efficiency of energy saving is PowerSaving=M-TM.

为使本发明的目的、特征和优点能够更加明显易懂，下面结合附图对本发明作进一步详细的说明。In order to make the objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings.

基于无线传感器网络的智能电网负载动态控制和分析方法的步骤流程如图2所示.智能电网的负载信息和数据都是通过智能电表进行采集的。智能电表采集的都是负载的实时数据，保证信息中心能够及时的得到负载当前的状况。然后，智能电表会周期性的以一定的速率向信息中心发送数据。数据发送的周期和速率是根据当前网络的状况决定的，不同的周期和速率可以影响通信网络的性能，从而影响智能电网的整体性能。当信息中心收到负载数据时，根据当前的信息，分析当前网络的性能，确定负载控制模型的各个参数和权重，从而得到适用于当前智能电网的负载控制和分析模型。信息中心按照当前的模型对智能电网的状况进行分析和预测，如果结果表明，当前的负载状况需要进行电能的重新调配，就会对电能进行重新调配，保证负载能够更有效率的应用电能，提高整个电网的能效性。反之，信息中心不会对电能进行重新分配。最后，信息中心周期性的对整个智能电网的节能状况进行统计和分析。The step flow of the smart grid load dynamic control and analysis method based on the wireless sensor network is shown in Figure 2. The load information and data of the smart grid are collected through smart meters. The smart meters collect real-time data of the load, ensuring that the information center can obtain the current status of the load in a timely manner. Then, the smart meter will periodically send data to the information center at a certain rate. The period and rate of data transmission are determined according to the current network conditions. Different periods and rates can affect the performance of the communication network, thereby affecting the overall performance of the smart grid. When the information center receives the load data, it analyzes the performance of the current network according to the current information, and determines the parameters and weights of the load control model, so as to obtain a load control and analysis model suitable for the current smart grid. The information center analyzes and predicts the status of the smart grid according to the current model. If the results show that the current load status requires power redistribution, the power will be redistributed to ensure that the load can use power more efficiently and improve Energy efficiency of the entire grid. On the contrary, the information center will not redistribute the electric energy. Finally, the information center periodically collects and analyzes the energy saving status of the entire smart grid.

图3为本发明中的智能电网的网络拓扑结构示意图。如图所示，G_i是第i个发电厂，S_ij表示第i个发电厂控制下的第j个汇聚节点，在该网络中，存在着不同类型的用户，存在一定数量的发电厂，同时每个发电厂为一定数量的用户提供电能，而且每个发电厂都是直接相连的。在智能电网中，存在着智能电表，它能够实时的监测用户的电量信息、收集数据并且通过无线传感器网络将数据信息传送到发电厂的智能控制和信息中心，智能控制和信息中心通过智能控制分析模型预测用户的未来用电状况，从而能够将一部分过剩的电能充分利用。一部分区域的用户的用电信息会通过该汇聚节点传送到发电厂的智能控制和信息中心。Fig. 3 is a schematic diagram of the network topology of the smart grid in the present invention. As shown in the figure, G _i is the i-th power plant, and S _ij represents the j-th convergence node under the control of the i-th power plant. In this network, there are different types of users and a certain number of power plants. At the same time, each power plant provides electricity to a certain number of users, and each power plant is directly connected. In the smart grid, there is a smart meter, which can monitor the user's power information in real time, collect data, and transmit the data information to the intelligent control and information center of the power plant through the wireless sensor network. The model predicts the user's future electricity consumption, so that a part of the excess electricity can be fully utilized. The electricity consumption information of users in a part of the area will be transmitted to the intelligent control and information center of the power plant through the aggregation node.

图4、图5为本发明中对智能电网的通信网络的分析性能图。从图中，可以发现用户的数量增多、智能电表发送数据的速率不同都可以改变通信网络的端到端时延和阻塞概率。具体分析如下：Fig. 4 and Fig. 5 are analysis performance diagrams of the communication network of the smart grid in the present invention. From the figure, it can be found that the increase in the number of users and the different data rates sent by smart meters can change the end-to-end delay and blocking probability of the communication network. The specific analysis is as follows:

针对每一个节点，平均时延为T_D，无线传感器网络的服务时间为T_S，网络中数据的等待时间为T_W，三者关系如下：For each node, the average delay is T _D , the service time of the wireless sensor network is T _S , and the waiting time of data in the network is T _W . The relationship between the three is as follows:

T_D=T_S+T_W(7)T _D =T _S +T _W (7)

其中，in,

T_S=T_L/T_V+T_C+T_P(8)T _S =T _L /T _V +T _C +T _P (8)

T_L是数据包的长度，T_V是数据的发送速率，T_C是MAC时延，T_P是无线传输时延。 _TL is the length of the data packet, T _V is the data transmission rate, T _C is the MAC delay, and T _P is the wireless transmission delay.

${T T}_{Delay Delay__overall overall} = = \underset{R R}{Σ Σ} (({Delay Delay}_{R R} \cdot &Center Dot; {γ γ}_{i i,, j j})) = = \underset{R R}{Σ Σ} ((\underset{((ij ij,, k k)) &Element; &Element; R R}{Σ Σ} (({T T}_{Ck C} + + {T T}_{Wk w} + + {T T}_{L L} / / {T T}_{V V})) \cdot &Center Dot; {γ γ}_{ij ij,, k k})) - - - - - - ((99))$

其中，in,

MAC时延的产生概率函数如下：The generation probability function of MAC delay is as follows:

${T T}_{MAC MAC} ((Z Z)) = = ((11 - - p p)) S S ((Z Z)) {Σ Σ}_{i i = = 00}^{L L} {{{[[pI pI ((Z Z))]]}^{i i} {Π Π}_{j j = = 00}^{i i} {D D.}_{j j} ((Z Z))}} + + {[[pI pI ((Z Z))]]}^{L L + + 11} {Π Π}_{i i = = 00}^{L L} {D D.}_{i i} ((Z Z)) - - - - - - ((1212))$

p=1-(1-τ)^n-1(13)p=1-(1-τ) ^n-1 (13)

$S S ((Z Z)) = = {Z Z}^{{T T}_{S S}},, I I ((Z Z)) = = {Z Z}^{{T T}_{I I}} and and {D D.}_{i i} ((Z Z)) = = \{\begin{matrix} {Σ Σ}_{i i = = 00}^{{CW CW}_{i i} - - 11} \frac{D D. ((Z Z))}{{CW CW}_{i i}},, 00 \leq \leq i i \leq \leq m m \\ {D D.}_{m m} ((Z Z)),, m m \leq \leq i i \leq \leq L L \end{matrix} - - - - - - ((1414))$

(15)(15)

E(T_MAC)=T′_MAC(Z)|_Z=1 E(T _MAC )=T′ _MAC (Z)| _Z=1

其中，in,

自变量Z是将队列上的离散问题进行Z变换后，将问题转化为Z域上变成连续的问题以便进行数学解决，变量Z本身没有什么实际意义；The independent variable Z is to transform the discrete problem on the queue into a continuous problem in the Z domain for mathematical solution, and the variable Z itself has no practical significance;

等待时间的计算如下：The waiting time is calculated as follows:

首先，定义到达负载的传输密度：First, define the transmission density to the load:

$ρ ρ = = λb λb = = \frac{λ λ}{μ μ} - - - - - - ((1717))$

η＝λ(1-P_k)(18)η=λ(1-P _k )(18)

E(X)=ηE(T)(19)E(X)=ηE(T)(19)

通过以上分析，我们可以得到无线传感器网络中的时延和阻塞概率的计算方法，从而分析整个网络的重要性能。如图4、图5所示，网络中的时延和阻塞随着用户的增多、发送速率的提高，会出现大幅度的增大。因此，在智能电网的网络拓扑设计当中，不能无限制的增加每个区域的用户数量，而是要合理的分布、使得通信网络处在最优化的状态当中，使得通信网络能够更好的支持和促进智能电网的性能，保证智能电网高效、高速的运行，提高能量的有效性，达到节能的效果。Through the above analysis, we can get the calculation method of time delay and blocking probability in the wireless sensor network, so as to analyze the important performance of the whole network. As shown in Figure 4 and Figure 5, the delay and congestion in the network will increase significantly with the increase of users and the increase of transmission rate. Therefore, in the network topology design of the smart grid, the number of users in each area cannot be increased without limit, but should be reasonably distributed so that the communication network is in an optimal state, so that the communication network can better support and Promote the performance of the smart grid, ensure the efficient and high-speed operation of the smart grid, improve the effectiveness of energy, and achieve energy-saving effects.

智能电网负载控制和信息中心采用的负载控制模型，在本发明中是相当重要的。动态负载分析和控制模型如下：The smart grid load control and the load control model adopted by the information center are quite important in the present invention. The dynamic load analysis and control model is as follows:

$C C = = {Σ Σ}_{i i = = 11}^{n no} {W W}_{i i} {S S}_{i i} - - - - - - ((1515))$

0≤W_i≤1，0≤S_i≤1(16)0≤W _i ≤1, 0≤S _i ≤1(16)

其中，S_i是影响智能电网用电效率的因素，W_i是对应影响因素的权重。Among them, S _i is the factor that affects the electricity efficiency of the smart grid, and W _i is the weight of the corresponding influencing factor.

在本发明中，由于实验环境有限，只考虑了重要的四个因素，具体模型如式（15）、（16）、（17）所示：In the present invention, due to the limited experimental environment, only four important factors are considered, and the specific models are shown in formulas (15), (16), and (17):

C=W_RS_R+W_VS_V+W_LS_L+W_PS_P C=W _R S _R +W _V S _V +W _L S _L +W _P S _P

(17)(17)

0≤S_R,S_V,S_L,S_P≤10≤S _R ,S _V ,S _L ,S _P ≤1

(18)(18)

0≤W_R,W_V,W_L,W_P≤1(19)0≤W _R ,W _V ,W _L ,W _P ≤1(19)

其中，in,

1.S_R是每个用户的负载用电量的过度的比例，表示为W_r是没有使用的电能，W_{max_r}是用户电能富裕的最大值。1. _{S R} is the excessive proportion of the load power consumption of each user, expressed as W _r is the unused electric energy, and W _{max_r} is the maximum value of the user's electric energy abundance.

2.S_v是每个用户的负载用电量的波动情况，表示为T_ave是用户用电状况不同状态之间的转变的时间间隔，T_{max_v}表示为用户用电状况不同状态之间的转变的最大时间间隔。2. S _v is the fluctuation of the load power consumption of each user, expressed as T _ave is the transition time interval between different states of the user's power consumption status, and T _{max_v} is expressed as the maximum time interval of the transition between different states of the user's power consumption status.

3.S_L是每个用户能量传输的损失比例，表示为d是发电厂和能量短缺的用户之间的距离，d_max是最大距离。3. S _L is the loss ratio of each user’s energy transmission, expressed as d is the distance between the power plant and the energy-deficient user, and _dmax is the maximum distance.

4.S_P是用户用电的优先级。4. S _P is the priority of the user's electricity consumption.

图6为随着节点数量的改变，整个智能电网的能量利用效率的性能比较图。其中，W_R=W_V=W_L=W_P=0.25，各种要素所占的重要性比例相同。图中整个网络中的用户数量从100逐渐递增到1000，可以看出，随着用户节点数量的增多，能量的利用效率是在不断的增加的，这主要是因为用户节点越多，电能使用情况能够互补的用户节点越多，剩余的电能也能被更加充分的利用。但是，当用户节点数量到达一点的数量的时候，整个网络中的能量利用率不会再有较大的增加，而出现较为平稳的态势。同时在图6中，采用了不同的用户状态改变的时间，分别是0.5小时、1小时、5小时、10小时、15小时。根据图中曲线，可以得出结论用户状态改变的平均时间越长，能量的利用率越高，最高可达到百分之十五。这也说明，用户的用电状态越稳定，即用户状态改变频率越低，越有利于提高能量的利用效率。Fig. 6 is a performance comparison graph of the energy utilization efficiency of the entire smart grid as the number of nodes changes. Among them, W _R =W _V =W _L =W _P =0.25, and the importance proportions of various elements are the same. The number of users in the entire network in the figure gradually increases from 100 to 1000. It can be seen that with the increase of the number of user nodes, the energy utilization efficiency is constantly increasing. This is mainly because the more user nodes, the lower the power consumption. The more user nodes that can complement each other, the more fully the remaining electric energy can be utilized. However, when the number of user nodes reaches a certain number, the energy utilization rate in the entire network will not increase significantly, and a relatively stable situation will appear. Meanwhile, in FIG. 6 , different user status change times are adopted, which are 0.5 hour, 1 hour, 5 hours, 10 hours, and 15 hours, respectively. According to the curve in the figure, it can be concluded that the longer the average time for user state changes, the higher the energy utilization rate, which can reach up to 15%. This also shows that the more stable the user's power consumption state is, that is, the lower the user state change frequency is, the more conducive to improving energy utilization efficiency.

图7为随着数据采集时间的改变，能量利用效率的性能比较图。由图中曲线可以看出，用户的状态越平稳、稳定，状态改变时间间隔越大，采集数据的时间间隔就越大，通信网络传输的数据就越少，当然对整个智能电网的能量利用率的提高就越有利。当用户的状态改变时间超过一定数值的时候，整个智能电网的能量利用率趋于平稳。Fig. 7 is a performance comparison graph of energy utilization efficiency as the data acquisition time changes. It can be seen from the curve in the figure that the more stable and stable the state of the user is, the greater the time interval between state changes, the longer the time interval of data collection, and the less data transmitted by the communication network. Of course, the energy utilization rate of the entire smart grid The improvement is more beneficial. When the user's state change time exceeds a certain value, the energy utilization rate of the entire smart grid tends to be stable.

图8为用户节点数量和数据发送速率的改变对能量的利用效率的影响比较图。图中3条曲线表示用户节点发送速率λ为10、30和60时的能量节约率。通过对比不同的发送速率和理想的发送速率状态下的能量节约的比例，得到如下结论：当用户节点数量较少的时候，用户节点使用不同的速率发送数据，没有导致性能出现较大的差别，都与理想状态的性能相当。FIG. 8 is a comparison diagram of the impact of changes in the number of user nodes and data transmission rates on energy utilization efficiency. The three curves in the figure represent the energy saving rate when the sending rate λ of the user node is 10, 30 and 60. By comparing the ratio of energy saving between different sending rates and the ideal sending rate state, the following conclusions are drawn: when the number of user nodes is small, the user nodes send data at different rates, which does not lead to a large difference in performance. Both are comparable to the ideal performance.

通过图4、图5可以看出，用户节点数量少，发送速率并不会导致网络的性能出现较大的波动，也并不影响智能电网中通信网络的实时数据的传送，既然也就不会影响智能电网的电能使用效率的整体性能。但随着用户节点数量的不断增大，用户节点使用不同的速率发送数据，整体性能就会出现较大的差别了。网络时延、阻塞、冲突的增大，就会对实时数据的传送出现比较大的影响。数据发送的速率越大，无线传感器网络的性能就会越差，导致数据不能实时的传送到信息控制中心，从而导致用户们的状态，如剩余电量或缺少电量，无法通过信息控制中心及时的进行平衡，因此就会导致智能电网的能量利用率越低。It can be seen from Figure 4 and Figure 5 that the number of user nodes is small, and the transmission rate will not cause large fluctuations in network performance, nor will it affect the real-time data transmission of the communication network in the smart grid, since it will not Affects the overall performance of the smart grid's power usage efficiency. However, as the number of user nodes continues to increase, user nodes use different rates to send data, and the overall performance will vary greatly. The increase of network delay, congestion, and conflict will have a relatively large impact on the transmission of real-time data. The greater the rate of data transmission, the worse the performance of the wireless sensor network will be, resulting in the data not being transmitted to the information control center in real time, resulting in the status of users, such as remaining power or lack of power, which cannot be monitored in a timely manner through the information control center. Balance, so it will lead to lower energy utilization of the smart grid.

图9为数据采集速率和发送速率的改变对能量的利用效率的影响比较图。该图中使得整体性能出现差别的因素主要是用户状态的改变、数据发送速率以及数据采集的速率。用户的用电状态越稳定、数据采集的速率就越小，需要传送的用电数据就越少，对整体的性能也就影响越小，另外，数据发送速率越小也会产生越小的影响。FIG. 9 is a comparison diagram of the impact of changes in the data collection rate and transmission rate on energy utilization efficiency. The main factors that make the difference in the overall performance in this figure are the change of user state, the rate of data sending and the rate of data acquisition. The more stable the user's power consumption status, the lower the data collection rate, the less power consumption data needs to be transmitted, and the smaller the impact on the overall performance. In addition, the lower the data transmission rate, the smaller the impact .

图10为数据发送速率和节点数量的改变对负载能量的需求控制的精准性。该图主要表现的是不同的数据发送速率和用户节点数量传送到信息控制中心的数据，然后再根据分析结果，对用户的用电情况进行预测和控制的结果。该过程中有可能把有些用户剩余的电量分配给附近的缺少电量的用户，实现了节省电量的结果。也有可能把电能传送给了不需要电能的用户，出现了失误。因此，信息控制中心对用户负载电能的控制精准率相当重要。从图中可以明显看出，用户节点的数量越大，数据发送速率越大，控制的精准性越差。Figure 10 shows the accuracy of the load energy demand control for changes in the data transmission rate and the number of nodes. This figure mainly shows the data sent to the information control center by different data transmission rates and the number of user nodes, and then according to the analysis results, the results of predicting and controlling the user's electricity consumption. In this process, it is possible to distribute the remaining power of some users to nearby users who lack power, thereby achieving the result of saving power. It is also possible that power is transmitted to users who do not need power, and mistakes have occurred. Therefore, it is very important for the information control center to control the accuracy of the user's load power. It can be clearly seen from the figure that the greater the number of user nodes, the greater the data transmission rate and the worse the control accuracy.

通过以上对不同因素如何影响能量节约率的具体分析，可以得出以下结论：理想状态下，数据采集频率越低、发送速率越小、数据阻塞与冲突越小，用户越少、用户状态越稳定、网络时延越小、智能电网的整体性能越好。但在实际应用中，必须因地制宜，对智能电网中的用户进行适当的区域分化，不同时段采用不同的数据采集、发送速率，与用户的状态、用户电量分配的精准性相适应，这实际上也是一个博弈的过程，使得智能电网的整体性能达到最优化。当然，随着智能电网的发展，需要考虑的因素将会越来越多，负载动态控制模型考虑的参数也将越来越多。Through the above specific analysis of how different factors affect the energy saving rate, the following conclusions can be drawn: ideally, the lower the data collection frequency, the lower the sending rate, the smaller the data congestion and conflict, the fewer users, and the more stable the user status , The smaller the network delay, the better the overall performance of the smart grid. However, in practical applications, users in the smart grid must be properly differentiated according to local conditions, and different data collection and transmission rates are used at different times to adapt to the user's status and the accuracy of user power distribution. A game process optimizes the overall performance of the smart grid. Of course, with the development of the smart grid, there will be more and more factors to be considered, and more and more parameters will be considered by the load dynamic control model.

Claims

1. A smart grid load dynamic control and analysis method, is characterized in that, comprises the following steps:

Communication network analysis: analyze the performance of the communication network of the smart grid according to the data periodically transmitted by the load combined with the data collected in real time, and obtain the performance of the elements that currently affect the performance of the smart grid;

Establishment of dynamic load analysis and control model: according to the obtained load information and the performance of the communication network, establish a corresponding dynamic load analysis and control model, analyze the current data of the load and the stored historical data, and predict the future power consumption status of the load ;

Load processing: Based on the results of dynamic load analysis and control model prediction, optimize the control of load power deployment;

The communication network analysis steps include:

Data collection and transmission steps, real-time collection of data information of smart grid loads and transmission of the collected information to the intelligent control analysis center;

The communication network performance analysis step is to analyze the impact of the data collection rate on the performance of the entire communication network according to the time delay and congestion of the communication network caused by the grid data information, data transmission and required rate, and then analyze the performance of the current communication network;

In the step of establishing the dynamic load analysis and control model, a dynamic load analysis and control model for user loads in the smart grid is established based on the obtained data information and factors affecting the efficiency of the smart grid;

In the load processing step, the intelligent control analysis center predicts the future power consumption information of the load based on the dynamic load analysis and control model results constructed, combined with the real-time data information of the load, and then analyzes the power consumption of users in the smart grid and then analyzes The power grid transmission situation is re-deployed;

In the load processing step, the factors that affect the power saving rate are analyzed include the frequency of data collection, the rate of data transmission, the number of nodes in the area, the frequency of node state changes, and network delay and congestion;

The dynamic load analysis and control model is as follows:

C C = = {Σ Σ}_{i i = = 11}^{n no} {W W}_{i i} {S S}_{i i} - - - - - - ((11))

0≤W _i ≤1,0≤S _i ≤1(2)

Among them, C represents the user's consumption index of electric energy, S _i is the factor affecting the efficiency of smart grid power consumption, W _i is the weight of the corresponding influencing factor,

The specific model is as follows:

C＝W _R S _R +W _V S _V +W _L S _L +W _P S _P (3)

0≤S _R ,S _V ,S _L ,S _P ≤1(4)

0≤W _R ,W _V ,W _L ,W _P ≤1(5)

W _R +W _V +W _L +W _P ＝1

(6)

in,

W _R , W _V , W _L , W _P represent the importance of the four factors S _R , S _V , S _L , and S _P that affect the power efficiency of the smart grid in the current analysis and prediction evaluation system. All factors The sum of corresponding weights W _R , W _V , W _L , W _P is equal to 1;

S _R is the excess proportion of the load power consumption of each user, expressed as W _r is the unused electric energy, and W _{max_r} is the maximum value of the user's electric energy abundance;

S _v is the fluctuation of the load power consumption of each user, expressed as T _ave is the time interval between transitions between different states of the user's power consumption status, and T _{max_v} is expressed as the maximum time interval between transitions between different states of the user's power consumption status;

_SL is the proportion of loss in energy transfer for each user, expressed as d is the distance between the power plant and the energy-deficient user, d _max is the maximum distance;

S _P is the priority of the user's electricity consumption.